By Sramana Mitra and guest author Shaloo Shalini
SM: Next in our discussion, I would like to explore entrepreneurial opportunities from your point of view in the supply chain domain. I am talking about the supply chain in general, and it sounds like you are reasonable experts in this domain. Where do you see entrepreneurial opportunities for starting new companies to solve open problems leveraging the cloud architecture?
SS: There is lot of opportunity in reporting and business intelligence, I would say. I know the general manager of the cloud services business, and all he talks about is metadata and canonized approaches to the cloud. People who are going to be successful are those who have metadata and a standardized approach figured out. Once you get into that, suddenly you have a very data-rich environment. I think there are some green spaces around all of that, because now you the capability to have this huge amount of standardized data coming from disparate sources whereby you can start to re-create useful business insights and business intelligence. I think that is one of the big areas for entrepreneurs.
SM: I agree with you. Actually, I think the organizing and streamlining of data opens up huge opportunities for business intelligence and reporting. To help our readers, can you give some use cases of where you think entrepreneurs can build solutions?
SS: Well, Jason, y go ahead if you have a couple on the top of your mind.
JP: Let me give you an example – it is a much-generalized used case – if you think about voting data. Voting data is collected within a county or state, or even regionally or nationally. There are multiple segments of those types of statistical data, which is perfect for applications to capture and store within a cloud to provide back to those agencies. Here is the reason why: if I’m collecting that data and storing it under a common format, every office within the state or area concerned has immediate access to that data in a standardized way at a moment’s notice. It allows you to grow that out regionally; it’s the same standardized data, or you even further; that is, nationally. It is still the same standardized data. Therefore, the ability to go after that data in a moment’s notice is what the key is. When I say moment’s notice, there is a lot that is involved because when you are talking about large data sets, and you have computing power to compound that data, and that’s costly. Using the cloud model, you can lower the cost to be able to provide that type of processing and to go through that size of data set.
SM: Jason, what you are pointing to is that in the context of an industry or is it a situation where the data sets have not yet been standardized. The opportunity there is not so much on analytics reporting yet because the data itself has not been standardized yet.
JP: That industry aspect, yes, that is correct. It is true for any industry; I mean, any industry is of course going to be moving toward standardized data.
SM: Different industries have different capacities for being standardized. If you look at the apparel industry, there is a certain behavior pattern, and trying to standardize the data set of the apparel industry is a different problem from standardizing the data set or taxonomies for the motor industry, right?
JP: That is right!
SS: We agree on that. It is a great point, Sramana, that you bring forth. We call these long-tail industries. If you look at automotive, industrial products, aviation, industrial fluid control, and bearing – they are very much long-tail industries. In fact, in the automotive industry products are still sold today that have been sold regularly for 55 or 60 years. In the case of these long-tail industries, it has been hard to get one’s arms around very large data sets. Now that we have standardized the data, we made a remarkable amount of progress. You can start to run queries and business intelligence about sales out, sales to, sales in, sell-through, and so forth. This is big in terms of bringing the kind of excellence that Wal-Mart shows, bringing that to the entire aftermarket. I think that Wal-Mart has proven in their model. If you look at what they did with retailing, sharing sell-through information with their suppliers and driving an entire category of their business, I think you will see that it is a big win for long-tail industries like this industry.